Building Mobius: Rethinking the AI Architecture for the Next Generation of Knowledge Work
At TILT Legal, we've seen both the immense promise and the difficult challenges that come with harnessing LLMs in professional services. One of the most challenging aspects of working with LLMs is their inherently stochastic nature. LLMs demonstrate a unique blend of of consistency and unpredictability. On a macro level, given a large enough sample size, we can often predict their behavior with a high degree of accuracy. However, every now and then, these models surprise us with unexpected outputs that can potentially interrupt an otherwise well-functioning automated workflow.
Another significant challenge lies in the technical complexity of implementing production-grade AI solutions. Most law firms, despite having deep legal expertise, lack the specialized engineering capabilities needed to build robust, secure, and scalable AI systems. This often results in either avoiding AI adoption altogether or implementing naive and shallow solutions that don't fully capture the transformative potential of generative AI in legal practice.
Our focus at TILT Legal has been twofold: narrowing the standard deviation of user experience – ensuring that our LLM-based solutions work reliably and accurately in real-life scenarios – and enabling professional services and law firms to leverage AI effectively in their practice.
The Problem with the Current Market for AI Solutions
Today's AI applications typically follow a predictable pattern: they're built around specific use cases, with hardcoded workflows and minimal customization options. While this approach works for simple, repetitive tasks, it falls apart when confronting the complex, context-dependent nature of knowledge work. Adoption of traditional point solutions also fail when they are applied in enterprise-wide contexts as they attempt to find product-process fit across diverse workflows.
Off-the-shelf AI tools present another significant limitation: they offer no competitive advantage in an increasingly crowded market. When every firm has access to the same standardized solutions, differentiation becomes impossible. What's needed is a paradigm shift towards platforms that allow firms to embed and leverage their unique intellectual property, domain expertise, and innovative methodologies. This enables firms to build truly distinctive service offerings that reflect their competitive advantages, rather than being constrained by pre-packaged solutions.
Unfortunately, many existing AI platform providers target large enterprises with pricing models that put their solutions out of reach for smaller firms and boutique practices. These platforms often require substantial upfront investments and ongoing costs that can be prohibitive for all but the largest organizations. This creates a barrier to entry that prevents many innovative smaller players from leveraging AI capabilities to compete effectively in the market.
Mobius takes a different approach: instead of creating another point solution, we've developed an extensible platform built on powerful primitives - think of them as sophisticated building blocks that can be combined in multiple ways to create customized workflows. These building blocks include:
- Advanced LLM interfaces for intelligent conversations and analysis
- Document generation and contract automation capabilities
- Document review and structured data extraction systems
What makes Mobius effective is its ability to combine these primitives in ways that match your firm's specific needs. Whether you're building client portals, automated workflow systems, or custom applications, Mobius provides the foundation while allowing you to maintain complete control over the user experience and branding.
Software and a Service: A Partnership Approach
Understanding that not all firms have internal engineering capabilities, Mobius includes an important component: our legal engineering service. Our team works alongside you to build, customize, and maintain your solutions. This dual offering - robust software combined with expert implementation support - ensures that firms of any size can leverage AI effectively.
Our partnership model goes beyond software implementation:
- Technical Expertise: Our legal engineers bridge the gap between legal practice and technology, bringing deep understanding of both domains
- Custom Development: From specialized workflows to practice-specific tools, we help build solutions that match your unique requirements
- Tailored support: In addition to providing ongoing maintenance, updates, and technical support, we provide tailored training programs, governance framework development, policy creation, and change management guidance - meeting each client at their current AI maturity level and helping them evolve
This collaborative approach ensures that firms can focus on their core legal work while having confidence that their AI infrastructure is being expertly managed and optimized. Whether you're just starting your AI journey or looking to enhance existing capabilities, our partnership model provides the support and expertise needed for successful implementation.
The 4 Pillars of Mobius
1. Data Foundation: Security and Privacy by Design
Our security-first data orchestration layer provides unified access across heterogeneous sources while ensuring strict privacy controls, data sovereignty, and enterprise-grade security compliance. This foundation enables safe and efficient AI operations while maintaining complete control over sensitive information.
We understand the importance of creating an environment where sensitive professional work can happen with complete confidence and control. Which is why at the core of Mobius lies our data orchestration layer. Privacy controls are deeply embedded in our system design through our "privacy by default" approach. This means all processing is ephemeral unless explicitly configured otherwise - data is processed in memory and immediately discarded once the task is complete. Any requirement for data persistence must be clearly justified and typically only occurs for specific audit or compliance purposes. This approach reflects our understanding that in professional services, data privacy and sovereignty aren't optional features - they're fundamental requirements.
Our architecture implements metadata management and standardized data models that enable consistent handling of information across different applications and use cases. The system supports both batch and real-time processing, allowing for flexible data operations while maintaining security integrity at every step.
We've built a unified access framework that seamlessly connects disparate data sources while maintaining strict security protocols e.g. using modern encryption practices.
2. AI Orchestration: Provider-Agnostic Intelligence
AI systems and workflows should be orchestrated through provider-agnostic frameworks that maintain sovereignty over data and execution while enabling dynamic optimization and transparency
The AI landscape is evolving rapidly, with new models and capabilities emerging constantly. Rather than locking you into a single provider, Mobius uses a provider-agnostic framework that allows dynamic selection of AI models based on cost, performance, and specific capabilities. This means firms can leverage the best tools for each task.
This provider-agnostic approach extends to deployment options as well. Organizations can choose to self-host models, integrate proprietary or open-source models, or use commercial API providers - giving them complete control over their AI infrastructure and intellectual property.
Our implementation of RAG (Retrieval-Augmented Generation) and multi-modal processing has also been a strategic choice that ensures knowledge workers can bring their entire information ecosystem to bear on complex problems.
3. Context-Based Applications: Bridging the Gap Between AI and Human
Access and usage patterns should be organized around natural business contexts and processes (matters, teams, practice areas) i.e.: Who (user/team), What (data/resource), Why (matter/purpose), When (temporal access), How (usage patterns)
Perhaps the most powerful aspect of Mobius is its context-based application layer. Instead of forcing users into predetermined workflows, Mobius organizes access and usage patterns around natural business contexts - who's working on what, why, and when. Through our system of composable primitives - fundamental building blocks like document automation, grounding, and batch processing - teams can rapidly deploy customized AI workflows that map directly to their specific needs.
Our legal engineers understand both the technology and the day-to-day realities of legal work. By analyzing existing processes and workflows, we craft solutions that feel intuitive rather than intrusive. This user-centric approach, combined with our dual expertise in law and technology, enables us to identify high-impact opportunities that genuinely elevate professional productivity—moving beyond simple automation to true workflow transformation.
We also strive to facilitate what we call "Augmentation Loops" - continuous cycles where human expertise configures and trains the AI, while AI capabilities amplify human knowledge work. By combining these primitives in different ways, teams can quickly iterate and extend their workflows, creating a system that gets more powerful over time.
4. Governance and Data-Driven Improvements
AI systems require continuous, multi-faceted monitoring that captures both technical performance and user interaction patterns to enable data-driven governance and optimization
At the top level, comprehensive monitoring and analytics ensure that the system is accountable. From LLM reasoning path analysis to hallucination detection, every aspect of the system's performance is transparent and measurable. This helps users to feel confident in their AI-assisted decisions while providing organizations with the oversight needed for responsible AI deployment. Analytics also illuminate opportunities for optimization, helping the system to improve over time.
Embedding robust governance features provides comfort to all stakeholders in the AI adoption journey. For executives and risk managers, it offers clear audit trails and compliance controls. For end users, it delivers transparency into AI decision-making processes. And for clients, it demonstrates a commitment to responsible AI usage that adheres to industry best practices. By implementing these comprehensive safeguards, organizations can confidently harness AI's transformative potential while maintaining the high standards of trust and accountability that professional services demand.
A New Paradigm for AI/Human Augmentation
We believe that the future of knowledge work isn't about replacing human expertise with AI, but about creating systems that enhance and amplify that expertise.
Every aspect of Mobius's design reflects this belief:
- Enterprise-grade security controls and data sovereignty are foundational, enabling confidential processing of sensitive information
- Human domain expertise is augmented in a context-specific way through configurable models, workflows, validation rules, and knowledge bases
- Continuous feedback loops help develop a symbiotic relationship between humans and AI by capturing user interactions and outcomes to refine system performance
- Human oversight is maintained through governance features including reasoning path analysis and observability tools that ensure transparency and accountability in AI-assisted decision making